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Performance regression in stat_ops.FrameMultiIndexOps.time_op #35186

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Performance regression in stat_ops.FrameMultiIndexOps.time_op #35186

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Performance regression in stat_ops.FrameMultiIndexOps.time_op
simonjayhawkins Jul 8, 2020
8138635
fixup for as_index=False
simonjayhawkins Jul 8, 2020
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22 changes: 12 additions & 10 deletions pandas/core/groupby/groupby.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1434,16 +1434,18 @@ def std(self, ddof: int = 1):
Series or DataFrame
Standard deviation of values within each group.
"""
return self._get_cythonized_result(
"group_var_float64",
aggregate=True,
needs_counts=True,
needs_values=True,
needs_2d=True,
cython_dtype=np.dtype(np.float64),
post_processing=lambda vals, inference: np.sqrt(vals),
ddof=ddof,
)
result = self.var(ddof=ddof)
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hmm you are reverting.

instead I would like to see if the generic function can be patched (e.g. something is fundamentally different).

cc @rhshadrach

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I don't see a way. _get_cythonized_result operates column-by-column, creating overhead. On data this size, we spend 25% in grouper.group_info and 40% in _wrap_aggregated_output. Within each of these operations, I don't see any easy wins. Only 27% of the time is spent doing the actual computation.

The only way I see to improve performance is to change _get_cythonized_result to a 2d operation, operating on all columns at once. In the PR that caused this regression, that's the first thing I tried and found that it got too hairy. In the process, I realized that perhaps a better solution would be to incorporate the features of _get_cythonized_result (namely, pre- and post-processing) into _cython_agg_general.

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hmm you are reverting.

yep. should have mentioned this in the OP.

instead I would like to see if the generic function can be patched (e.g. something is fundamentally different).

OK. will close this for now. can reopen if a better solution is not forthcoming before the release.

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OK. will close this for now. can reopen if a better solution is not forthcoming before the release.

or maybe better would be to backport this after 1.1rc if not fixed before.

if result.ndim == 1:
result = np.sqrt(result)
else:
cols = result.columns.get_indexer_for(
result.columns.difference(self.exclusions).unique()
)
# TODO(GH-22046) - setting with iloc broken if labels are not unique
# .values to remove labels
result.iloc[:, cols] = np.sqrt(result.iloc[:, cols]).values

return result

@Substitution(name="groupby")
@Appender(_common_see_also)
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